@article{ART003306407},
author={Dong-Kun Jung and KIM HUNHEE},
title={An Empirical Study on a Market Regime-Adaptive Hybrid GA-ESN based Algorithmic Trading System for Stock Price Prediction: Evidence from DIA, QQQ, SPY, and KOSPI200},
journal={Journal of Internet of Things and Convergence},
issn={2466-0078},
year={2026},
volume={12},
number={1},
pages={127-137}
TY - JOUR
AU - Dong-Kun Jung
AU - KIM HUNHEE
TI - An Empirical Study on a Market Regime-Adaptive Hybrid GA-ESN based Algorithmic Trading System for Stock Price Prediction: Evidence from DIA, QQQ, SPY, and KOSPI200
JO - Journal of Internet of Things and Convergence
PY - 2026
VL - 12
IS - 1
PB - The Korea Internet of Things Society
SP - 127
EP - 137
SN - 2466-0078
AB - Traditional technical analysis methods rely on empirically chosen parameters, which limits their effectiveness in improving prediction accuracy and trading stability for nonlinear and non-stationary financial time series. This study proposes a market regime-adaptive Hybrid GA–ESN (Genetic Algorithm–Echo State Network)-based algorithmic trading system for stock price prediction. A genetic algorithm is employed to automatically optimize key parameters of the Golden/Dead Cross (GC), Envelope Moving Average (ENV), and Relative Strength Index (RSI), mitigating parameter-sensitivity issues. Smoothed prices and a triangle target representation for turning points are then used as input features to the ESN. In addition, the market is classified into four regimes according to trend and volatility, and a cross-validation-based ESN is adopted to enhance adaptability to changing market conditions. Empirical results for the DIA, QQQ, SPY, and KOSPI200 indices demonstrate that the proposed Hybrid GA–ESN model outperforms the Buy-and-Hold strategy and static ESN models in terms of both return and maximum drawdown.
KW - Algorithmic Trading;Stock Price Prediction;Hybrid Model;Genetic Algorithm (GA);Echo State Network (ESN);Market Regime Classification
DO -
UR -
ER -
Dong-Kun Jung and KIM HUNHEE. (2026). An Empirical Study on a Market Regime-Adaptive Hybrid GA-ESN based Algorithmic Trading System for Stock Price Prediction: Evidence from DIA, QQQ, SPY, and KOSPI200. Journal of Internet of Things and Convergence, 12(1), 127-137.
Dong-Kun Jung and KIM HUNHEE. 2026, "An Empirical Study on a Market Regime-Adaptive Hybrid GA-ESN based Algorithmic Trading System for Stock Price Prediction: Evidence from DIA, QQQ, SPY, and KOSPI200", Journal of Internet of Things and Convergence, vol.12, no.1 pp.127-137.
Dong-Kun Jung, KIM HUNHEE "An Empirical Study on a Market Regime-Adaptive Hybrid GA-ESN based Algorithmic Trading System for Stock Price Prediction: Evidence from DIA, QQQ, SPY, and KOSPI200" Journal of Internet of Things and Convergence 12.1 pp.127-137 (2026) : 127.
Dong-Kun Jung, KIM HUNHEE. An Empirical Study on a Market Regime-Adaptive Hybrid GA-ESN based Algorithmic Trading System for Stock Price Prediction: Evidence from DIA, QQQ, SPY, and KOSPI200. 2026; 12(1), 127-137.
Dong-Kun Jung and KIM HUNHEE. "An Empirical Study on a Market Regime-Adaptive Hybrid GA-ESN based Algorithmic Trading System for Stock Price Prediction: Evidence from DIA, QQQ, SPY, and KOSPI200" Journal of Internet of Things and Convergence 12, no.1 (2026) : 127-137.
Dong-Kun Jung; KIM HUNHEE. An Empirical Study on a Market Regime-Adaptive Hybrid GA-ESN based Algorithmic Trading System for Stock Price Prediction: Evidence from DIA, QQQ, SPY, and KOSPI200. Journal of Internet of Things and Convergence, 12(1), 127-137.
Dong-Kun Jung; KIM HUNHEE. An Empirical Study on a Market Regime-Adaptive Hybrid GA-ESN based Algorithmic Trading System for Stock Price Prediction: Evidence from DIA, QQQ, SPY, and KOSPI200. Journal of Internet of Things and Convergence. 2026; 12(1) 127-137.
Dong-Kun Jung, KIM HUNHEE. An Empirical Study on a Market Regime-Adaptive Hybrid GA-ESN based Algorithmic Trading System for Stock Price Prediction: Evidence from DIA, QQQ, SPY, and KOSPI200. 2026; 12(1), 127-137.
Dong-Kun Jung and KIM HUNHEE. "An Empirical Study on a Market Regime-Adaptive Hybrid GA-ESN based Algorithmic Trading System for Stock Price Prediction: Evidence from DIA, QQQ, SPY, and KOSPI200" Journal of Internet of Things and Convergence 12, no.1 (2026) : 127-137.
